ITK  5.2.0
Insight Toolkit
Examples/RegistrationITKv4/ImageRegistration9.cxx
/*=========================================================================
*
* Copyright NumFOCUS
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0.txt
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
*=========================================================================*/
// Software Guide : BeginCommandLineArgs
// INPUTS: {BrainProtonDensitySliceBorder20.png}
// INPUTS: {BrainProtonDensitySliceR10X13Y17.png}
// OUTPUTS: {ImageRegistration9Output.png}
// OUTPUTS: {ImageRegistration9DifferenceBefore.png}
// OUTPUTS: {ImageRegistration9DifferenceAfter.png}
// ARGUMENTS: 1.0 300
// Software Guide : EndCommandLineArgs
// Software Guide : BeginLatex
//
// This example illustrates the use of the \doxygen{AffineTransform}
// for performing registration in $2D$. The example code is, for the most
// part, identical to that in \ref{sec:InitializingRegistrationWithMoments}.
// The main difference is the use of the AffineTransform here instead of the
// \doxygen{Euler2DTransform}. We will focus on the most
// relevant changes in the current code and skip the basic elements already
// explained in previous examples.
//
// \index{itk::AffineTransform}
//
// Software Guide : EndLatex
// Software Guide : BeginLatex
//
// Let's start by including the header file of the AffineTransform.
//
// \index{itk::AffineTransform!header}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
//
// The following piece of code implements an observer
// that will monitor the evolution of the registration process.
//
#include "itkCommand.h"
class CommandIterationUpdate : public itk::Command
{
public:
using Self = CommandIterationUpdate;
using Superclass = itk::Command;
using Pointer = itk::SmartPointer<Self>;
itkNewMacro(Self);
protected:
CommandIterationUpdate() = default;
public:
using OptimizerPointer = const OptimizerType *;
void
Execute(itk::Object * caller, const itk::EventObject & event) override
{
Execute((const itk::Object *)caller, event);
}
void
Execute(const itk::Object * object, const itk::EventObject & event) override
{
auto optimizer = static_cast<OptimizerPointer>(object);
if (!itk::IterationEvent().CheckEvent(&event))
{
return;
}
std::cout << optimizer->GetCurrentIteration() << " ";
std::cout << optimizer->GetValue() << " ";
std::cout << optimizer->GetCurrentPosition();
// Print the angle for the trace plot
vnl_matrix<double> p(2, 2);
p[0][0] = (double)optimizer->GetCurrentPosition()[0];
p[0][1] = (double)optimizer->GetCurrentPosition()[1];
p[1][0] = (double)optimizer->GetCurrentPosition()[2];
p[1][1] = (double)optimizer->GetCurrentPosition()[3];
vnl_svd<double> svd(p);
vnl_matrix<double> r(2, 2);
r = svd.U() * vnl_transpose(svd.V());
double angle = std::asin(r[1][0]);
std::cout << " AffineAngle: " << angle * 180.0 / itk::Math::pi
<< std::endl;
}
};
int
main(int argc, char * argv[])
{
if (argc < 4)
{
std::cerr << "Missing Parameters " << std::endl;
std::cerr << "Usage: " << argv[0];
std::cerr << " fixedImageFile movingImageFile " << std::endl;
std::cerr << " outputImagefile [differenceBeforeRegistration] "
<< std::endl;
std::cerr << " [differenceAfterRegistration] " << std::endl;
std::cerr << " [stepLength] [maxNumberOfIterations] " << std::endl;
return EXIT_FAILURE;
}
// Software Guide : BeginLatex
//
// We then define the types of the images to be registered.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
constexpr unsigned int Dimension = 2;
using PixelType = float;
using FixedImageType = itk::Image<PixelType, Dimension>;
using MovingImageType = itk::Image<PixelType, Dimension>;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The transform type is instantiated using the code below. The template
// parameters of this class are the representation type of the space
// coordinates and the space dimension.
//
// \index{itk::AffineTransform!Instantiation}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
using MetricType =
using RegistrationType = itk::
ImageRegistrationMethodv4<FixedImageType, MovingImageType, TransformType>;
MetricType::Pointer metric = MetricType::New();
OptimizerType::Pointer optimizer = OptimizerType::New();
RegistrationType::Pointer registration = RegistrationType::New();
registration->SetMetric(metric);
registration->SetOptimizer(optimizer);
// Software Guide : BeginLatex
//
// The transform object is constructed below and is initialized before the
// registration process starts.
//
// \index{itk::AffineTransform!New()}
// \index{itk::AffineTransform!Pointer}
// \index{itk::RegistrationMethodv4!SetTransform()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
TransformType::Pointer transform = TransformType::New();
// Software Guide : EndCodeSnippet
using FixedImageReaderType = itk::ImageFileReader<FixedImageType>;
using MovingImageReaderType = itk::ImageFileReader<MovingImageType>;
FixedImageReaderType::Pointer fixedImageReader =
FixedImageReaderType::New();
MovingImageReaderType::Pointer movingImageReader =
MovingImageReaderType::New();
fixedImageReader->SetFileName(argv[1]);
movingImageReader->SetFileName(argv[2]);
registration->SetFixedImage(fixedImageReader->GetOutput());
registration->SetMovingImage(movingImageReader->GetOutput());
// Software Guide : BeginLatex
//
// In this example, we again use the
// \doxygen{CenteredTransformInitializer} helper class in order to compute
// reasonable values for the initial center of rotation and the
// translations. The initializer is set to use the center of mass of each
// image as the initial correspondence correction.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using TransformInitializerType =
FixedImageType,
MovingImageType>;
TransformInitializerType::Pointer initializer =
TransformInitializerType::New();
initializer->SetTransform(transform);
initializer->SetFixedImage(fixedImageReader->GetOutput());
initializer->SetMovingImage(movingImageReader->GetOutput());
initializer->MomentsOn();
initializer->InitializeTransform();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Now we pass the transform object to the registration filter, and it will
// be grafted to the output transform of the registration filter by
// updating its parameters during the the registration process.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
registration->SetInitialTransform(transform);
registration->InPlaceOn();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Keeping in mind that the scale of units in scaling, rotation and
// translation are quite different, we take advantage of the scaling
// functionality provided by the optimizers. We know that the first $N
// \times N$ elements of the parameters array correspond to the rotation
// matrix factor, and the last $N$ are the components of the translation to
// be applied after multiplication with the matrix is performed.
//
// Software Guide : EndLatex
double translationScale = 1.0 / 1000.0;
if (argc > 8)
{
translationScale = std::stod(argv[8]);
}
// Software Guide : BeginCodeSnippet
using OptimizerScalesType = OptimizerType::ScalesType;
OptimizerScalesType optimizerScales(transform->GetNumberOfParameters());
optimizerScales[0] = 1.0;
optimizerScales[1] = 1.0;
optimizerScales[2] = 1.0;
optimizerScales[3] = 1.0;
optimizerScales[4] = translationScale;
optimizerScales[5] = translationScale;
optimizer->SetScales(optimizerScales);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// We also set the usual parameters of the optimization method. In this
// case we are using an
// \doxygen{RegularStepGradientDescentOptimizerv4} as before. Below, we
// define the optimization parameters like learning rate (initial step
// length), minimum step length and number of iterations. These last two
// act as stopping criteria for the optimization.
//
// Software Guide : EndLatex
double steplength = 1.0;
if (argc > 6)
{
steplength = std::stod(argv[6]);
}
unsigned int maxNumberOfIterations = 300;
if (argc > 7)
{
maxNumberOfIterations = std::stoi(argv[7]);
}
// Software Guide : BeginCodeSnippet
optimizer->SetLearningRate(steplength);
optimizer->SetMinimumStepLength(0.0001);
optimizer->SetNumberOfIterations(maxNumberOfIterations);
// Software Guide : EndCodeSnippet
// Create the Command observer and register it with the optimizer.
//
CommandIterationUpdate::Pointer observer = CommandIterationUpdate::New();
optimizer->AddObserver(itk::IterationEvent(), observer);
// One level registration process without shrinking and smoothing.
//
constexpr unsigned int numberOfLevels = 1;
RegistrationType::ShrinkFactorsArrayType shrinkFactorsPerLevel;
shrinkFactorsPerLevel.SetSize(1);
shrinkFactorsPerLevel[0] = 1;
RegistrationType::SmoothingSigmasArrayType smoothingSigmasPerLevel;
smoothingSigmasPerLevel.SetSize(1);
smoothingSigmasPerLevel[0] = 0;
registration->SetNumberOfLevels(numberOfLevels);
registration->SetSmoothingSigmasPerLevel(smoothingSigmasPerLevel);
registration->SetShrinkFactorsPerLevel(shrinkFactorsPerLevel);
// Software Guide : BeginLatex
//
// Finally we trigger the execution of the registration method by calling
// the \code{Update()} method. The call is placed in a \code{try/catch}
// block in the case any exceptions are thrown.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
try
{
registration->Update();
std::cout << "Optimizer stop condition: "
<< registration->GetOptimizer()->GetStopConditionDescription()
<< std::endl;
}
catch (const itk::ExceptionObject & err)
{
std::cerr << "ExceptionObject caught !" << std::endl;
std::cerr << err << std::endl;
return EXIT_FAILURE;
}
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Once the optimization converges, we recover the parameters from the
// registration method. We can also recover the
// final value of the metric with the \code{GetValue()} method and the
// final number of iterations with the \code{GetCurrentIteration()}
// method.
//
// \index{itk::RegistrationMethodv4!GetValue()}
// \index{itk::RegistrationMethodv4!GetCurrentIteration()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
const TransformType::ParametersType finalParameters =
registration->GetOutput()->Get()->GetParameters();
const double finalRotationCenterX = transform->GetCenter()[0];
const double finalRotationCenterY = transform->GetCenter()[1];
const double finalTranslationX = finalParameters[4];
const double finalTranslationY = finalParameters[5];
const unsigned int numberOfIterations = optimizer->GetCurrentIteration();
const double bestValue = optimizer->GetValue();
// Software Guide : EndCodeSnippet
// Print out results
//
std::cout << "Result = " << std::endl;
std::cout << " Center X = " << finalRotationCenterX << std::endl;
std::cout << " Center Y = " << finalRotationCenterY << std::endl;
std::cout << " Translation X = " << finalTranslationX << std::endl;
std::cout << " Translation Y = " << finalTranslationY << std::endl;
std::cout << " Iterations = " << numberOfIterations << std::endl;
std::cout << " Metric value = " << bestValue << std::endl;
// Compute the rotation angle and scaling from SVD of the matrix
// \todo Find a way to figure out if the scales are along X or along Y.
// VNL returns the eigenvalues ordered from largest to smallest.
vnl_matrix<double> p(2, 2);
p[0][0] = (double)finalParameters[0];
p[0][1] = (double)finalParameters[1];
p[1][0] = (double)finalParameters[2];
p[1][1] = (double)finalParameters[3];
vnl_svd<double> svd(p);
vnl_matrix<double> r(2, 2);
r = svd.U() * vnl_transpose(svd.V());
double angle = std::asin(r[1][0]);
const double angleInDegrees = angle * 180.0 / itk::Math::pi;
std::cout << " Scale 1 = " << svd.W(0) << std::endl;
std::cout << " Scale 2 = " << svd.W(1) << std::endl;
std::cout << " Angle (degrees) = " << angleInDegrees << std::endl;
// Software Guide : BeginLatex
//
// Let's execute this example over two of the images provided in
// \code{Examples/Data}:
//
// \begin{itemize}
// \item \code{BrainProtonDensitySliceBorder20.png}
// \item \code{BrainProtonDensitySliceR10X13Y17.png}
// \end{itemize}
//
// The second image is the result of intentionally rotating the first
// image by $10$ degrees and then translating by $(-13,-17)$. Both images
// have unit-spacing and are shown in Figure
// \ref{fig:FixedMovingImageRegistration9}. We execute the code using the
// following parameters: step length=1.0, translation scale= 0.0001 and
// maximum number of iterations = 300. With these images and parameters
// the registration takes $92$ iterations and produces
//
// \begin{center}
// \begin{verbatim}
// 90 44.0851 [0.9849, -0.1729, 0.1725, 0.9848, 12.4541, 16.0759]
// AffineAngle: 9.9494
// \end{verbatim}
// \end{center}
//
// These results are interpreted as
//
// \begin{itemize}
// \item Iterations = 92
// \item Final Metric = 44.0386
// \item Center = $( 111.204, 131.591 )$ millimeters
// \item Translation = $( 12.4542, 16.076 )$ millimeters
// \item Affine scales = $(1.00014, .999732)$
// \end{itemize}
//
// The second component of the matrix values is usually associated with
// $\sin{\theta}$. We obtain the rotation through SVD of the affine
// matrix. The value is $9.9494$ degrees, which is approximately the
// intentional misalignment of $10.0$ degrees.
//
// \begin{figure}
// \center
// \includegraphics[width=0.44\textwidth]{BrainProtonDensitySliceBorder20}
// \includegraphics[width=0.44\textwidth]{BrainProtonDensitySliceR10X13Y17}
// \itkcaption[AffineTransform registration]{Fixed and moving images
// provided as input to the registration method using the AffineTransform.}
// \label{fig:FixedMovingImageRegistration9}
// \end{figure}
//
//
// \begin{figure}
// \center
// \includegraphics[width=0.32\textwidth]{ImageRegistration9Output}
// \includegraphics[width=0.32\textwidth]{ImageRegistration9DifferenceBefore}
// \includegraphics[width=0.32\textwidth]{ImageRegistration9DifferenceAfter}
// \itkcaption[AffineTransform output images]{The resampled moving image
// (left), and the difference between the fixed and moving images before
// (center) and after (right) registration with the AffineTransform
// transform.} \label{fig:ImageRegistration9Outputs} \end{figure}
//
// Figure \ref{fig:ImageRegistration9Outputs} shows the output of the
// registration. The right most image of this figure shows the squared
// magnitude difference between the fixed image and the resampled
// moving image.
//
// \begin{figure}
// \center
// \includegraphics[height=0.32\textwidth]{ImageRegistration9TraceMetric}
// \includegraphics[height=0.32\textwidth]{ImageRegistration9TraceAngle}
// \includegraphics[height=0.32\textwidth]{ImageRegistration9TraceTranslations}
// \itkcaption[AffineTransform output plots]{Metric values,
// rotation angle and translations during the registration using the
// AffineTransform transform.}
// \label{fig:ImageRegistration9Plots}
// \end{figure}
//
// Figure \ref{fig:ImageRegistration9Plots} shows the plots of the main
// output parameters of the registration process. The metric values at
// every iteration are shown on the left plot. The angle values are shown
// on the middle plot, while the translation components of the registration
// are presented on the right plot. Note that the final total offset of the
// transform is to be computed as a combination of the shift due to
// rotation plus the explicit translation set on the transform.
//
// Software Guide : EndLatex
// The following code is used to dump output images to files.
// They illustrate the final results of the registration.
// We will resample the moving image and write out the difference image
// before and after registration. We will also rescale the intensities of
// the difference images, so that they look better!
using ResampleFilterType =
ResampleFilterType::Pointer resampler = ResampleFilterType::New();
resampler->SetTransform(transform);
resampler->SetInput(movingImageReader->GetOutput());
FixedImageType::Pointer fixedImage = fixedImageReader->GetOutput();
resampler->SetSize(fixedImage->GetLargestPossibleRegion().GetSize());
resampler->SetOutputOrigin(fixedImage->GetOrigin());
resampler->SetOutputSpacing(fixedImage->GetSpacing());
resampler->SetOutputDirection(fixedImage->GetDirection());
resampler->SetDefaultPixelValue(100);
using OutputPixelType = unsigned char;
using OutputImageType = itk::Image<OutputPixelType, Dimension>;
using CastFilterType =
WriterType::Pointer writer = WriterType::New();
CastFilterType::Pointer caster = CastFilterType::New();
writer->SetFileName(argv[3]);
caster->SetInput(resampler->GetOutput());
writer->SetInput(caster->GetOutput());
writer->Update();
using DifferenceFilterType =
DifferenceFilterType::Pointer difference = DifferenceFilterType::New();
difference->SetInput1(fixedImageReader->GetOutput());
difference->SetInput2(resampler->GetOutput());
WriterType::Pointer writer2 = WriterType::New();
using RescalerType =
RescalerType::Pointer intensityRescaler = RescalerType::New();
intensityRescaler->SetInput(difference->GetOutput());
intensityRescaler->SetOutputMinimum(0);
intensityRescaler->SetOutputMaximum(255);
writer2->SetInput(intensityRescaler->GetOutput());
resampler->SetDefaultPixelValue(1);
// Compute the difference image between the
// fixed and resampled moving image.
if (argc > 5)
{
writer2->SetFileName(argv[5]);
writer2->Update();
}
using IdentityTransformType = itk::IdentityTransform<double, Dimension>;
IdentityTransformType::Pointer identity = IdentityTransformType::New();
// Compute the difference image between the
// fixed and moving image before registration.
if (argc > 4)
{
resampler->SetTransform(identity);
writer2->SetFileName(argv[4]);
writer2->Update();
}
return EXIT_SUCCESS;
}
itk::CastImageFilter
Casts input pixels to output pixel type.
Definition: itkCastImageFilter.h:104
itkRegularStepGradientDescentOptimizerv4.h
itk::IdentityTransform
Implementation of an Identity Transform.
Definition: itkIdentityTransform.h:50
itkCenteredTransformInitializer.h
itkImageFileReader.h
itk::SmartPointer< Self >
itkCastImageFilter.h
itkAffineTransform.h
itk::AffineTransform
Definition: itkAffineTransform.h:101
itkImageRegistrationMethodv4.h
itkMeanSquaresImageToImageMetricv4.h
itk::ImageFileReader
Data source that reads image data from a single file.
Definition: itkImageFileReader.h:75
itk::RegularStepGradientDescentOptimizerv4
Regular Step Gradient descent optimizer.
Definition: itkRegularStepGradientDescentOptimizerv4.h:47
itk::Command
Superclass for callback/observer methods.
Definition: itkCommand.h:45
itk::ImageFileWriter
Writes image data to a single file.
Definition: itkImageFileWriter.h:87
itk::Command
class ITK_FORWARD_EXPORT Command
Definition: itkObject.h:43
itkSubtractImageFilter.h
itk::Command::Execute
virtual void Execute(Object *caller, const EventObject &event)=0
itkRescaleIntensityImageFilter.h
itk::SubtractImageFilter
Pixel-wise subtraction of two images.
Definition: itkSubtractImageFilter.h:68
itkImageFileWriter.h
itk::MeanSquaresImageToImageMetricv4
Class implementing a mean squares metric.
Definition: itkMeanSquaresImageToImageMetricv4.h:46
itk::ResampleImageFilter
Resample an image via a coordinate transform.
Definition: itkResampleImageFilter.h:90
itk::Object
Base class for most ITK classes.
Definition: itkObject.h:62
itk::RescaleIntensityImageFilter
Applies a linear transformation to the intensity levels of the input Image.
Definition: itkRescaleIntensityImageFilter.h:154
itk::Image
Templated n-dimensional image class.
Definition: itkImage.h:86
itk::EventObject
Abstraction of the Events used to communicating among filters and with GUIs.
Definition: itkEventObject.h:57
itkResampleImageFilter.h
itk::Math::pi
static constexpr double pi
Definition: itkMath.h:64
itk::GTest::TypedefsAndConstructors::Dimension2::Dimension
constexpr unsigned int Dimension
Definition: itkGTestTypedefsAndConstructors.h:44
itkCommand.h
itk::CenteredTransformInitializer
CenteredTransformInitializer is a helper class intended to initialize the center of rotation and the ...
Definition: itkCenteredTransformInitializer.h:61